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Analyzing Beauty by Building Custom Profiles Using Machine Learning

机译:通过使用机器学习构建自定义配置文件来分析美丽

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Instead of tackling what is universal beauty, we propose to attempt to create profiles based on training data for individual understanding of beauty, while maintaining efficiency. This paper describes a fully functional software system called TBeauty that creates an individual machine-learning algorithm based off a profile to allow users to get an accurate assessment of what they would classify and score beauty. The system would take data and use facial landmarks in order to assess this. The system can be changed to allow automatic search of new images to proactively select the ones that have high scores for any individual user. The project uses an existing 68-point landmark detection algorithm proposed by Rainer Lienhart to identify the facial landmark points (a so-called facial mask) from images that will be fed into various machine-learning algorithms. The software is available on GitHub.
机译:我们建议不要尝试解决普遍的美,而是尝试根据训练数据创建概要文件,以个人理解美,同时保持效率。本文介绍了一个名为TBeauty的功能齐全的软件系统,该系统基于配置文件创建了一个单独的机器学习算法,以使用户能够准确评估自己对美容的分类和评分。该系统将获取数据并使用面部标志来评估这一点。可以更改系统以允许自动搜索新图像,以主动选择对任何单个用户而言得分较高的图像。该项目使用了Rainer Lienhart提出的现有的68点界标检测算法,以从图像中识别出面部界标点(所谓的面罩),这些图像将被馈送到各种机器学习算法中。该软件可在GitHub上获得。

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